Chairperson Overview, Jessica Faust, Deloitte Consulting
Clinical Trials Innovations in the Age of Big Data and Advanced Analytics
Clinical trials operations have historically been a domain rich in data as by nature clinical trials are heavily regulated processes that entail data collection. As a result, during the entire life cycle of a trial enormous amounts of data are collected and stored in all phases, from selection of sites to monitoring and auditing to ensure quality and compliance. This has culminated in sponsors of trials having the unique opportunity to leverage big data and advanced analytics to optimize and improve clinical trial operations at a time when advanced analytics is coming of age. At Janssen, the data sciences group in partnership with global clinical operations has launched initiatives in site selection, risk-based monitoring, and quality and compliance. This presentation will delve on aspects of this work and present vignettes to highlight the challenges and successes.
Cloud Based Open Ecosystem Approaches to Accelerate Time to Insights for Translational Research
Fundamental shifts in biomedical research and development would involve new models for biologic and biomedical research to drive innovation, support value-based healthcare delivery, and to be able to fulfill the promise of precision medicine. Deloitte began a collaborative journey to create a cloud based fully-integrated research analysis platform hosted on Amazon web services to support a number of critical Translational Science research objectives of a major global client. The goal of this initiative was to transform vast quantities of data into meaningful intelligence capable of providing clinical and business insight across the entire Research and Development value chain. A comprehensive strategy was implemented to bridge the chasm from silos of data to an interconnected data hub that can be interrogated and transformed through advanced analytics. Clinical data, real world data, bio-specimen and genomic data was housed in disconnected silos, concealing key insights that could be gained through integrated data mining and modeling across programs and projects. Our cloud based [platform efficiently connects these silos of high quality data to powerful analytics and visualization tools that enable researchers to easily explore hypotheses and discover novel biological and clinical relationships leading to deeper understanding of patient-stratified diseases and treatments. The integrated platform reduces the amount of time to answer complex questions from months and weeks to minutes.
Workshop: Real World Evidence and Deep Learning Models
View details HERE
Session Introduction featuring IBM Aspera
A Dose of Pragmatism: Keep the Artificial Intelligence and Machine Learning Buzz on a Tight Leash When We Talk Healthcare
AI and ML have replaced Big Data conversation in healthcare as the latter failed to produce any meaningful impact at a reasonable cost. Euphoria is erupting in the innovation community about the potential for those technology. How does it actually look when we see from inside out vs outside in? Do we have too much technology than what we need while we are burning out providers? What are the challenges facing us in healthcare today and what could be a pragmatic approach to apply those technology solutions in healthcare? How can the solutions become affordable for at scale deployment and in which areas? Coming from the healthcare safety net market this talk will share a pragmatic approach to implement newest technologies for a different segment of the market.
Supporting a Collaborative R&D Organization with a Dynamic Big Data Solution
The clinical development process involves tight coordination across R&D to successfully bring a drug through the approval process. Unfortunately, disparate data in clinical systems create difficulties in understanding how trials are performing. We’ll review a use case where a cloud-enabled, scalable environment was implemented to perform data lifecycle management, analytics, and intuitive reporting including: Conceptual data landscape and flow of information Look at various analytic solutions and how they are built for various objectives Reflect on learnings and possible innovations Using the right analytic solution provides tremendous benefits including faster time to commercialization and better business and patient outcomes
Workforce Analytics: Driving Organizational Success
Healthcare is a labor-intensive industry. To provide high quality and affordable service to our member and community, Kaiser Permanente relies heavily on workforce analytics to determine staffing need and to tackle workforce challenges. The Workforce Analytics team gather data, build analytical models, and consult with various stakeholders to formulate staffing strategy that enhances organizational effectiveness. In my presentation, I will go through the work process, demonstrate some work samples and share the story behind our team development.
Fighting Diabetes by finding the "Persuadable's"
At Omada, we strive to maximize weight loss outcomes using a digital behavior change program. As Omada scales health coaching, machine learning is a powerful tool to increase efficiency. Tina will share learnings from one of Omada's most successful data-driven product features. This presentation will cover the product journey of the feature: from mapping the problem, to collecting training data and building the model, to A/B testing prototypes and finally, to measuring results.
Registration & Breakfast
Technology Enabled Clinical Decision Support at the Point of Care
The cost of care keeps increasing. The outcomes are not getting better proportionately. Using risk stratification to align the resources to outcomes provides a way to decrease cost and increase quality outcomes. Implementation of electronic health records has created large amounts of data. Risk stratification of patients is now possible using this data. Giving healthcare providers access to this information at the point of care helps them make decisions that deliver better outcomes.
New Ways Technology Will Bridge the Continuum of Care
The way we prescribe, inspire, monitor, and report physical activity and healthy behavior is quickly evolving. From consumer wearable devices and smartphone applications, to advances in sensors and software, we can now track protocol adherence from the doctor’s office across the entire lifespan of a treatment regimen or exercise prescription. Through advances in facial recognition and machine learning, much of this can now be accomplished completely passively (without the use of wearables). In this talk, Dr. Rucker presents how the gym of the future will forge stronger partnerships with health care providers through the use of disruptive technology currently being designed for the modern health club.
The Life of a Model: Getting from Conception to the Runway to Retirement Without Breaking the Bank
NorthShore University HealthSystem owes part of its operational success to efficient integration of its business intelligence and predictive analytics solution into operational workflows. Daniel's presentation will focus on the implementation of best practices for full predictive model development life cycle from conceptualization to implementation to retirement, complete with real-life examples, including comprehensive adverse patient outcomes and cost and utilization models. Daniel will offer practical suggestions on how to minimize the effort required to embed a model into the operational process, maximize the value-added for the organization, and facilitate the life cycle maintenance and obsolescence of predictive models already in production.
Developing an Advanced Customer Optimization Strategy to Drive Business Growth
Cancer immunotherapy offers great promise where biomarkers and big data have been shown to predict therapy outcome in various types of cancer patients. The talk will describe the development and clinical application of PWS database and DeepSea algorithm in support of RNA+DNA liquid biopsyin cancer immunotherapy and targeted therapy. Case studies will be presented in personalized cancer care and clinical drug development.